확률및통계 2주차 강의

sin(90)

a <- 1
b <- 2
c <- 3

x <- a+b+c

a

var.name1 <- 1
var1.name <- 1
._var.name <- 10

var.name1
print(var.name1)

cat(a,b,c) # a, b, c 병합

ls(all.names = TRUE) # 모든 이름들


a <- 10
b <- 15
c <- 20

z <- a + b + c
a-b
b-a
a-b+c
a/b
b/a
a*b

b%%a # 15 mod 10

a>b
a<b

c >= b

b>=b

a==b
a==a
b==b

z == (a+b+c)

a != b
b != a
a != a


x <- seq(100, 0, -2)
x
x2 <- seq(100,0)
x2

x <- c(1:3)
y <- rep(x, times=2) #123123 덩어리로 반복
y

y <- rep(x, each=3) # 111222333 각각을 반복
y

d <- 1:10
sum(d)
sum(2*d)
length(d)
mean(d)
max(d[1:5])
sort(d)
sort(d, decreasing = TRUE)
s <- sort(d, decreasing = TRUE)
s
median(d)  # 중앙값
range(d)  # 범위
sort(d, TRUE)

bt <- c("A", 'B','B','0','AB','A')
bt
bt.new <- factor(bt)  # 4개의 레벨을 가지고있음

gender <- c('F','M','F','F','M')
gender.new <- factor(gender)
bt.new  # 값들과 Levels 나옴

is.factor(bt.new) # factor가 맞는 지 확임
is.factor(bt)

bt[5]
bt.new[5]
as.integer(bt.new)

bt.new[7] <- "B"
bt.new

x <- 1:4
y <- 5:8
z <- matrix(1:20, nrow=4)
z


m1 <- cbind(x,y)  # 행방향으로 결합
m1

m2 <- rbind(x,y) # 열방향으로 결합
m2

m3 <- rbind(m2, x)
m3

m4 <- cbind(z, x)
m4


d <- "Thid is \"KGP TALKIE\" channel"
d

z[3,3]
z[4,5]
z[2,]
z[,4]

z[2:3, 1:3]
z[,seq(1,5,2)]  # 1,3,5 열만 추출
z[,c(1,3,5)]    # 1,3,5 열만 추출

days <- c('mon','tue','wed','thu','fri','sat','sun')
temp <- c(12, 13, 11, 10.5, 14, 10, 13.1)
hum <- c(80, 56, 78, 78, 45, 87, 91)
sky <- c('sunny', 'cloudy', 'sunny', 'cloudy', 'sunny', 'sunny', 'cloudy')

df <- data.frame(days, temp, hum, sky)
is.data.frame(df)

head(df, 2)
tail(df, 2)
sample_n(df, 2)
str(df)
summary(df)

df['days']
df[['days']]
class(df['days'])
class(df[['days']])

df$days
df[1,1]
df
df[1,]
df$days=='thu'

df[df$temp>12,]

class(iris)
class(state.x77)


#data <- read.csv("covid_19_data_cleaned.csv")
data <- read.csv(file.choose())
view(data)